An Efficient Coding Method for Spike Camera using Inter-Spike Intervals
This work addresses a storage and transmission bottleneck for spike cameras, which are useful for capturing fast-moving scenes, but it is incremental as it builds on existing coding techniques.
The paper tackles the problem of transmitting or storing large amounts of spike data from bio-inspired spike cameras by proposing an efficient lossy coding method, which integrates adaptive temporal partitioning, prediction, quantization, and entropy coding, and demonstrates effectiveness in compressing spike data while maintaining fidelity on a constructed PKU-Spike dataset.
Recently, a novel bio-inspired spike camera has been proposed, which continuously accumulates luminance intensity and fires spikes while the dispatch threshold is reached. Compared to the conventional frame-based cameras and the emerging dynamic vision sensors, the spike camera has shown great advantages in capturing fast-moving scene in a frame-free manner with full texture reconstruction capabilities. However, it is difficult to transmit or store the large amount of spike data. To address this problem, we first investigate the spatiotemporal distribution of inter-spike intervals and propose an intensity-based measurement of spike train distance. Then, we design an efficient spike coding method, which integrates the techniques of adaptive temporal partitioning, intra-/inter-pixel prediction, quantization and entropy coding into a unified lossy coding framework. Finally, we construct a PKU-Spike dataset captured by the spike camera to evaluate the compression performance. The experimental results on the dataset demonstrate that the proposed approach is effective in compressing such spike data while maintaining the fidelity.